Computational Analysis of Plasma Lipidomics from Mice Fed Standard Chow and Ketogenic Diet

Dietary saturated fatty acids (SFAs) are upregulated in the blood circulation following digestion. A variety of circulating lipid species have been implicated in metabolic and inflammatory diseases; however, due to the extreme variability in serum or plasma lipid concentrations found in human studies, established reference ranges are still lacking, in addition to lipid specificity and diagnostic biomarkers. Mass spectrometry is widely used for identification of lipid species in the plasma, and there are many differences in sample extraction methods within the literature. We used ultra-high performance liquid chromatography (UPLC) coupled to a high-resolution hybrid triple quadrupole-time-of-flight (QToF) mass spectrometry (MS) to compare relative peak abundance of specific lipid species within the following lipid classes: free fatty acids (FFAs), triglycerides (TAGs), phosphatidylcholines (PCs), and sphingolipids (SGs), in the plasma of mice fed a standard chow (SC; low in SFAs) or ketogenic diet (KD; high in SFAs) for two weeks. In this protocol, we used Principal Component Analysis (PCA) and R to visualize how individual mice clustered together according to their diet, and we found that KD-fed mice displayed unique blood profiles for many lipid species identified within each lipid class compared to SC-fed mice. We conclude that two weeks of KD feeding is sufficient to significantly alter circulating lipids, with PCs being the most altered lipid class, followed by SGs, TAGs, and FFAs, including palmitic acid (PA) and PA-saturated lipids. This protocol is needed to advance knowledge on the impact that SFA-enriched diets have on concentrations of specific lipids in the blood that are known to be associated with metabolic and inflammatory diseases. Key features • Analysis of relative plasma lipid concentrations from mice on different diets using R. • Lipidomics data collected via ultra-high performance liquid chromatography (UPLC) coupled to a high-resolution hybrid triple quadrupole-time-of-flight (QToF) mass spectrometry (MS). • Allows for a comprehensive comparison of diet-dependent plasma lipid profiles, including a variety of specific lipid species within several different lipid classes. • Accumulation of certain free fatty acids, phosphatidylcholines, triglycerides, and sphingolipids are associated with metabolic and inflammatory diseases, and plasma concentrations may be clinically useful.

This protocol is used in: eLife (2022), DOI: 10.7554/eLife.76744Dietary saturated fatty acids (SFAs) are upregulated in the blood circulation following digestion.A variety of circulating lipid species have been implicated in metabolic and inflammatory diseases; however, due to the extreme variability in serum or plasma lipid concentrations found in human studies, established reference ranges are still lacking, in addition to lipid specificity and diagnostic biomarkers.Mass spectrometry is widely used for identification of lipid species in the plasma, and there are many differences in sample extraction methods within the literature.We used ultra-high performance liquid chromatography (UPLC) coupled to a high-resolution hybrid triple quadrupole-time-of-flight (QToF) mass spectrometry (MS) to compare relative peak abundance of specific lipid species within the following lipid classes: free fatty acids (FFAs), triglycerides (TAGs), phosphatidylcholines (PCs), and sphingolipids (SGs), in the plasma of mice fed a standard chow (SC; low in SFAs) or ketogenic diet (KD; high in SFAs) for two weeks.In this protocol, we used Principal Component Analysis (PCA) and R to visualize how individual mice clustered together according to their diet, and we found that KD-fed mice displayed unique blood profiles for many lipid species identified within each lipid class compared to SC-fed mice.We conclude that two weeks of KD feeding is sufficient to significantly alter circulating lipids, with PCs being the most altered lipid class, followed by SGs, TAGs, and FFAs, including palmitic acid (PA) and PA-saturated lipids.This protocol is needed to advance knowledge on the impact that SFA-enriched diets have on concentrations of specific lipids in the blood that are known to be associated with metabolic and inflammatory diseases.double bonds, the separation and identification of isobaric lipids that have identical molecular formulas but structural differences are limited (Batarseh et al., 2018).For instance, TAG (16:0/18:1/20:4) and TAG (18:1/18:2/18:2) are isobaric lipids.These lipids are chromatographically separated and identified by MS/MS fragmentation.However, TAG (16:0/18:1/18:2) and TAG (16:0/18:2/18:1) are not chromatographically separated with no difference in MS/MS spectrum because of the same fatty acyl group on different locations of the glycerol backbone (Figure 1).The ion mobility or ozone-induced dissociation techniques can afford information on double bond and location of fatty acyl group on lipids.Lastly, aside from essential FAs that are known to be only derived from exogenous sources (omega-3 and omega-6), lipidomics data does not indicate whether plasma lipids entered the bloodstream directly following digestion of lipids or were produced endogenously by the host.Thus, proper controls are required when studying diet-dependent effects.

Materials and reagents
Biological materials Note: The KD is soft and requires refrigeration and daily food changes; cages must be cleaned or replaced every three days.3. Immediately prior to euthanizing mice, prepare heparin solution (~5 mL) and coat syringes for cardiac punctures by aspirating and expelling the solution with each syringe.The same heparin solution can be used for multiple syringes.After coating, syringes may be placed on a sterile surface resting on needle caps.Ensure that the needles are bevel up and ready for use.4. Prepare blood collection tubes and ice bucket and set tabletop centrifuge temperature to 4 °C. 5. Euthanize mice one at a time with CO2 and prior to cervical dislocation, place mouse supine on bench, and quickly perform cardiac puncture using 3 mL BD Luer-Lok syringe with attached 25 G needle.
It is important that the needle gauge used for this procedure is between 23 and 25 G, in order to avoid hemolysis.Hemolysis is the destruction of red blood cells, and it has been shown to significantly impact levels of certain lipid species in the blood (Burla et al., 2018).6. Transfer blood (200-700 μL depending on size of mouse) to BD Vacutainer blood collection tubes and keep on ice.7. Transfer blood samples to microcentrifuge tubes and centrifuge at 1,500× g for 20 min at 4 °C.8. Collect transparent plasma (supernatant) and transfer to a fresh microcentrifuge tube.

Data analysis
Identification and quantification of lipidomics  Choi et al., 2015).Raw MS files (*.wiff) were imported and processed by the program PeakView (Sciex) (Figure 2, Figure 3).PeakView detects spectral features using extract ion chromatogram (XIC) lists from our in-house library of lipids (each defined by a unique chromatographic retention time and accurate mass, MS/MS fragmentation, and isotopic pattern; Figure 3, B and C).Each peak was integrated by MultiQuant (Sciex) software (Figure 4).Peak integration is the quantification step whereby the peak area of an identified lipid is calculated.Peak area is referred to in this protocol as the AUC and this value is proportional to the quantity of the identified lipid.2. Save lipidomics data as Excel or .csvfiles.Normalization can be done in Excel with the following formula: peak area ratio = peak area of identified lipid/peak area of labeled internal standard.Each integrated chromatogram was normalized manually with the use of an internal standard peak purchased from Avanti lipids (SPLASH LipidoMix) (Figure 4).

Bioinformatic analysis in R
1.In R, scale each lipid type or class dataset with the scale() function.For mean centering, replace NA values with the mean of the particular variable.For more detailed information on the code see General note 4. 2. Perform a PCA analysis with the prcomp() function.
PCA analysis is a technique for multivariable data that performs dimension reduction to represent the most important and impactful information as principal components.This allows the data to be visually observed for patterns of similarity (Abdi and Williams, 2010).3. Visualize the first two principal components for the dataset for each lipid type with the fviz_pca_ind() function from the factoextra package (Kassambara and Mundt, 2017).Color samples by diet group and create a confidence ellipse around each sample group (Figure 5A).Set addEllipses = TRUE, ellipse.type= "confidence," and ellipse.level= 0.95 to create confidence ellipses.4. Investigate strong separation between groups in the 2D space indicated by no overlap between the concentration ellipses.Sample groups with low separation for lipid types will have overlapping ellipses and data points.5. Investigate specific lipid variables contributing to separation with fviz_pca_biplot() to create a biplot (Wickham, 2016) (Figure 5B).a. Choose the top five contributing variables to view with select.var= list(contrib = 5).Adjust the number to increase or decrease the desired number of variables.b.Phosphatidylcholine (PC) variables are lipids identified within each plasma sample represented by vectors that are close together and that form small angles with one another because they are positively correlated.Variables point in the direction of the principal component(s) they strongly influence.In the PC biplot, all of the variables point along the PC1 axis towards the KD samples, indicating that the KD samples are relatively more enriched in these PCs.This enrichment strongly contributes to the separation of the KD and SC, as the SC samples group towards the opposite side of PC1, away from where the variables are pointing.6. Perform a heatmap analysis with hierarchical clustering using the pheatmap() function on the dataset (Kolde, 2018).Identify lipid types with clear clustering between diet groups (Figure 5C). 7. Perform t-tests in R or, alternatively, GraphPad Prism to compare significance of lipids between sample diet groups (Table 1).

Figure 5 .
Figure 5. Lipidomics data analysis (see General note 4).(A) PCA plot of phosphatidylcholine composition identified via LC-QToF MS/MS between age-matched (6-8 weeks) BALB/c female mice.Dots represent individual mice with colors corresponding to a standard chow (SC) (grey) or ketogenic diet (KD) (orange) with the mean of each diet group surrounded by a 95% confidence ellipse.(B) Biplot labeled with the top five phosphatidylcholines contributing to sample separation in 2D space.(C) Heatmap analysis of phosphatidylcholine composition between individual mice.